With the ubiquity of the internet, it is now commonplace for users to conduct searches for a variety of products and services, including their medical providers (e.g., doctors, specialists, etc.) In various existing applications, these users (also referred to herein as members) searching for a provider in a search application for care are shown providers based on geographic proximity, ranked by some aggregated provider level cost efficiency or based on other aggregated measures specific to providers. However, this process does not ensure that the ranking is personalized for the individual member. For example, a member can have a combination of disease conditions, habits, and family history of diseases, or might have interacted with a certain set of providers, which resulted in successful maintenance of good health or well managed disease conditions, etc. Ranking providers only based on a general or highly aggregated subset high-level provider information (without taking granular member level information, provider level information or member provider interactions into account) can easily embed the right provider for the member in lower ranks and surface “sub-optimal” providers for the members on top.
Thus, there is a need for a provider search with predicting providers for an individual member given a member's conditions, past interaction history and information regarding how providers treated members with various conditions, along with volumes of patients they treated for these conditions, etc.